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max_pool_v3.h 2.9 kB

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  1. /**
  2. * Copyright 2019-2020 Huawei Technologies Co., Ltd
  3. *
  4. * Licensed under the Apache License, Version 2.0 (the "License");
  5. * you may not use this file except in compliance with the License.
  6. * You may obtain a copy of the License at
  7. *
  8. * http://www.apache.org/licenses/LICENSE-2.0
  9. *
  10. * Unless required by applicable law or agreed to in writing, software
  11. * distributed under the License is distributed on an "AS IS" BASIS,
  12. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13. * See the License for the specific language governing permissions and
  14. * limitations under the License.
  15. */
  16. #ifndef OPS_BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_H_
  17. #define BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_H_
  18. #include "graph/operator_reg.h"
  19. namespace ge {
  20. /**
  21. * @brief Performs max pooling on the input . \n
  22. * @par Inputs:
  23. * One input:
  24. * x: An NC1HWC0 Tensor. Supported type:float16, float32, double, int8, int16,
  25. * int32, int64, uint8, uint16, qint8
  26. * @par Attributes:
  27. * @li ksize: A required list of int8, int16, int32, or int64 values,
  28. * specifying the size of the window for each dimension of the input tensor.
  29. * No default value.
  30. * @li strides: A required list of int8, int16, int32, or int64 values,
  31. * specifying the stride of the sliding window for each dimension of
  32. * the input tensor. No default value.
  33. * @li padding_mode: A required string. Defaults to "CALCULATED".
  34. * @li pads:A required list of int8, int16, int32, or int64 values,
  35. * a data to caculate when padding_mode is "SAME" and "CALCULATED".
  36. * @li data_format: An optional string. Defaults to "NHWC" .
  37. * @li global_pooling bool, Whether to use the global pooling.
  38. * If global_pooling = true, kernel size and paddings will be ignored.
  39. * Default False
  40. * @li ceil_mode:global_pooling (bool) – (bool) Whether to use the global pooling.
  41. * If global_pooling = true, kernel size and paddings will be ignored.
  42. * Default False \n
  43. * @par Outputs:
  44. * y: A Tensor. Has the same type and format as input "x" . \n
  45. * @attention Constraints:
  46. * @li "ksize" is a list that has length 4: ksize[0] = 1 or ksize[3] = 1,
  47. * ksize[1] * ksize[2] <= 255.
  48. * @li "stride is a list that has length 4: strides[0] = 1 or strides[3] = 1,
  49. * strides[1] <= 63, strides[0] >= 1, strides[2] <= 63, strides[2] >= 1.
  50. * @li "padding" is "SAME" "VALID" or "CACULATE" .
  51. * @par Third-party framework compatibility
  52. * Compatible with the TensorFlow operator MaxPool.
  53. */
  54. REG_OP(MaxPoolV3)
  55. .INPUT(x,TensorType({DT_FLOAT16, DT_FLOAT32}))
  56. .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT32}))
  57. .REQUIRED_ATTR(ksize, ListInt)
  58. .REQUIRED_ATTR(strides, ListInt)
  59. .ATTR(padding_mode, String, "CALCULATED")
  60. .ATTR(pads, ListInt, {0,0,0,0})
  61. .ATTR(data_format, String, "NCHW")
  62. .ATTR(global_pooling,Bool,false)
  63. .ATTR(ceil_mode, Bool, false)
  64. .OP_END_FACTORY_REG(MaxPoolV3)
  65. } // namespace ge
  66. #endif // OPS_BUILT_IN_OP_PROTO_INC_MAX_POOL_V3_H_

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示